认知无线网络资源管理的研究
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摘要
认知无线电技术是一种用户根据其周围的无线环境变化来调整工作参数以提高频谱利用率的新技术。资源管理作为认知无线网络中一项重要的关键技术，已成为当前的研究热点。本文在系统分析认知无线网络技术原理和研究现状的基础上，开展对认知无线网络的资源管理技术的研究，取得了以下具有创新性的研究成果：
1）针对认知无线网络可用信道在工作频率、带宽、传输范围和干扰范围上的特性差异，提出一种基于跨层优化框架并用于联合设计认知无线网络的频谱分配、调度以及路由的算法。该算法的特点在于利用已知的信道特性信息构建调度矩阵，把NP-hard的混合型整数线性规划的优化问题简化成普通的线性规划问题，并提出一种遍历的算法，把构建调度矩阵的计算复杂度从O(2M)减少到O(M2)的数量级。仿真结果表明该算法可以提高认知无线网络的性能。
2）针对认知无线网络中信息有效性及网络环境的时变特性，提出基于SINR物理干扰模型的分布式资源管理算法。首先采用基于解偶分层的方法把网络效用最大化问题分解成拥塞控制子问题和调度子问题，在此基础上提出一种联合路由、动态频谱分配、调度及发射功率控制的分布式优化算法求解调度子问题以最大化网络吞吐量。该算法的创新点在于联合考虑认知无线网络可用频谱动态变化的特性以及节点间的数据包队列长度的变化，提出一种无冲突调度协议，让具有较高的节点间待传数据包队列长度之差且具有较高信道容量的链路优先发送数据包，以提高网络吞吐量。仿真结果表明在网络负荷较大的场景下该算法可以有效地提高网络的吞吐量。
3）针对认知无线网络的实时业务提出一种以最小化业务流端到端时延为目标的分布式资源管理算法。该算法把节点的策略定义为路由的选择和频道的选择，采用基于SINR的物理干扰模型，由各节点自主地利用从邻居节点收集到的待接收节点的干扰信号强度来计算本节点的可行策略（包括可用的频道和可行的下一跳接收节点）以及经过可行的下一跳节点到达目的地节点的预计时延，从而为流经本节点的业务流选择具有最小时延的策略，以达到最小化业务流端到端时延的目标。该算法的创新点是在选择可用频道和路由的时候联合考虑可用链路容量以及下一跳节点到目的地的时延的影响，从而能够获得更小的端到端时延和丢包率。仿真结果验证了该算法的有效性。
The concept of cognitive radio is proposed to improve spectrum efficiency since itequips wireless users with the capability to optimally adapt their operating parametersaccording to the interactions with the surrounding radio environment. The resourcemanagement in cognitive radio networks has been receiving an increasing attention in recentyears.
Based on the survey on the fundamentals of cognitive radio networking technology andrecent advances in related research, the resource management in cognitive radio networks isinvestigated and the contributions and innovations are achieved as follows:
1) To address the wireless channel heterogeneity, which is a unique feature in cognitiveradio networks, a cross-layer optimization framework is presented, which jointly considersthe spectrum sharing, scheduling and routing with the objective of minimizing the systemtotal activation time, i.e., maximizing the system capacity. A novel algorithm is proposed togenerate all of the system concurrent transmission link-channel sets. Based on that, the MILP(Mixed Integer Linear Programming) optimization framework is simplified to a LP (LinearProgramming) problem, so the global optimal solution could be easily found. Thecomputational complexity to construct the schedule matrix is simplied from O(2M) to O(M2)by the traversal algorithm. Simulation results verify the significant benefit for the cross-layerspectrum sharing in cognitive networks.
2) To address the dynamic nature of the wireless channel and the network environment, aSINR-interference-model-based algorithm is proposed for resource management. Firstly, thenetwork utility maximization problem is decoupled into the congestion-control problem andthe scheduling problem. And then a distributed algorithm is presented to find the maximalthroughput of the network. The innovated idea is that not only the differential backlogbetween neighborhood nodes but also the capacity of the wireless channel are used fordetermining the priority of sending packets, and a conflict-free MAC is proposed to improvethe network performance. Simulation results show the throughput is improved when thenetwork is heavily loaded.
3) In order to minimize the end-to-end packet delay for delay-sensitive applications, aSINR-interference-model-based algorithm is proposed to manage network resources. It allowsnodes to exchange local information with the neighbors, and find their own feasible actionsincluding the relay node and the transmission frequency channel, and then autonomously select the optimal one with minimal expect delay to destination. The distributed algorithm isshown through numerical simulations to outperform other solutions leading to a lowerend-to-end packet delay and packet loss rate.
1）针对认知无线网络可用信道在工作频率、带宽、传输范围和干扰范围上的特性差异，提出一种基于跨层优化框架并用于联合设计认知无线网络的频谱分配、调度以及路由的算法。该算法的特点在于利用已知的信道特性信息构建调度矩阵，把NP-hard的混合型整数线性规划的优化问题简化成普通的线性规划问题，并提出一种遍历的算法，把构建调度矩阵的计算复杂度从O(2M)减少到O(M2)的数量级。仿真结果表明该算法可以提高认知无线网络的性能。
2）针对认知无线网络中信息有效性及网络环境的时变特性，提出基于SINR物理干扰模型的分布式资源管理算法。首先采用基于解偶分层的方法把网络效用最大化问题分解成拥塞控制子问题和调度子问题，在此基础上提出一种联合路由、动态频谱分配、调度及发射功率控制的分布式优化算法求解调度子问题以最大化网络吞吐量。该算法的创新点在于联合考虑认知无线网络可用频谱动态变化的特性以及节点间的数据包队列长度的变化，提出一种无冲突调度协议，让具有较高的节点间待传数据包队列长度之差且具有较高信道容量的链路优先发送数据包，以提高网络吞吐量。仿真结果表明在网络负荷较大的场景下该算法可以有效地提高网络的吞吐量。
3）针对认知无线网络的实时业务提出一种以最小化业务流端到端时延为目标的分布式资源管理算法。该算法把节点的策略定义为路由的选择和频道的选择，采用基于SINR的物理干扰模型，由各节点自主地利用从邻居节点收集到的待接收节点的干扰信号强度来计算本节点的可行策略（包括可用的频道和可行的下一跳接收节点）以及经过可行的下一跳节点到达目的地节点的预计时延，从而为流经本节点的业务流选择具有最小时延的策略，以达到最小化业务流端到端时延的目标。该算法的创新点是在选择可用频道和路由的时候联合考虑可用链路容量以及下一跳节点到目的地的时延的影响，从而能够获得更小的端到端时延和丢包率。仿真结果验证了该算法的有效性。
The concept of cognitive radio is proposed to improve spectrum efficiency since itequips wireless users with the capability to optimally adapt their operating parametersaccording to the interactions with the surrounding radio environment. The resourcemanagement in cognitive radio networks has been receiving an increasing attention in recentyears.
Based on the survey on the fundamentals of cognitive radio networking technology andrecent advances in related research, the resource management in cognitive radio networks isinvestigated and the contributions and innovations are achieved as follows:
1) To address the wireless channel heterogeneity, which is a unique feature in cognitiveradio networks, a cross-layer optimization framework is presented, which jointly considersthe spectrum sharing, scheduling and routing with the objective of minimizing the systemtotal activation time, i.e., maximizing the system capacity. A novel algorithm is proposed togenerate all of the system concurrent transmission link-channel sets. Based on that, the MILP(Mixed Integer Linear Programming) optimization framework is simplified to a LP (LinearProgramming) problem, so the global optimal solution could be easily found. Thecomputational complexity to construct the schedule matrix is simplied from O(2M) to O(M2)by the traversal algorithm. Simulation results verify the significant benefit for the cross-layerspectrum sharing in cognitive networks.
2) To address the dynamic nature of the wireless channel and the network environment, aSINR-interference-model-based algorithm is proposed for resource management. Firstly, thenetwork utility maximization problem is decoupled into the congestion-control problem andthe scheduling problem. And then a distributed algorithm is presented to find the maximalthroughput of the network. The innovated idea is that not only the differential backlogbetween neighborhood nodes but also the capacity of the wireless channel are used fordetermining the priority of sending packets, and a conflict-free MAC is proposed to improvethe network performance. Simulation results show the throughput is improved when thenetwork is heavily loaded.
3) In order to minimize the end-to-end packet delay for delay-sensitive applications, aSINR-interference-model-based algorithm is proposed to manage network resources. It allowsnodes to exchange local information with the neighbors, and find their own feasible actionsincluding the relay node and the transmission frequency channel, and then autonomously select the optimal one with minimal expect delay to destination. The distributed algorithm isshown through numerical simulations to outperform other solutions leading to a lowerend-to-end packet delay and packet loss rate.
引文
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